Retrieving Data
The Screening and Risk Factors category of cancerprof contains 6 unique functions to pull data from the Screening and Risk Factor page of State Cancer Profile.
These functions are: risk_alcohol(),
risk_colorectal_screening(),
risk_diet_exercise(), risk_smoking(),
risk_vaccines(), risk_womens_health()
Each of these functions require various parameters that must be specified to pull data. Please refer to function documentation for more details.
Risk Alcohol
Risk Alcohol requires 3 arguments: alcohol,
race, sex
alcohol1 <- risk_alcohol(
alcohol = paste(
"binge drinking (4+ drinks on one occasion for women,",
"5+ drinks for one occasion for men), ages 21+"
),
race = "all races (includes hispanic)",
sex = "both sexes"
)
head(alcohol1, n = 3)
#> State FIPS Percent Lower_95%_CI Upper_95%_CI Number_of_Respondents
#> 1 District of Columbia 11001 26.2 23.9 28.4 566
#> 2 North Dakota 38000 22.8 21.1 24.5 676
#> 3 Iowa 19000 21.9 20.7 23.1 1515Risk Colorectal Screening
Risk Colorectal Screening has 4 arguments: screening,
race, sex, area
"home blood stool test in the past year, ages 45-75" and
"received at least one recommended crc test, ages 45-75"
for the screening arguments requires a race argument and a
sex argument and defaults to
"direct estimates", "US by state".
"ever had fobt, ages 50-75",
"guidance sufficient crc, ages 50-75",
"had colonoscopy in past 10 years, ages 50-75" for the
screening arguments defaults to "all races",
"both sexes", and
"county level modeled estimates".
screening1 <- risk_colorectal_screening(
screening = "home blood stool test in the past year, ages 45-75",
race = "all races (includes hispanic)",
sex = "both sexes"
)
head(screening1, n = 3)
#> State FIPS Percent Lower_95%_CI Upper_95%_CI Number_of_Respondents
#> 1 Wyoming 56000 3.0 2.2 3.7 75
#> 2 Mississippi 28000 3.4 2.3 4.5 64
#> 3 Delaware 10000 3.8 3.0 4.7 106
screening2 <- risk_colorectal_screening(
screening = "ever had fobt, ages 50-75",
area = "usa"
)
head(screening2, n = 3)
#> County FIPS Model_Based_Percent (95%_Confidence_Interval) Lower_95%_CI Upper_95%_CI
#> 1 New Hanover County 37129 0.2 0 1.2
#> 2 Columbus County 37047 0.3 0 1.5
#> 3 Dixon County 31051 0.3 0 1.5Risk Diet-Exercise
Risk Diet-Exercise requires 3 arguments: diet_exercise ,
race, sex
diet_exercise1 <- risk_diet_exercise(
diet_exercise = "bmi is healthy, ages 20+",
race = "all races (includes hispanic)",
sex = "both sexes"
)
head(diet_exercise1, n = 3)
#> State FIPS Percent Lower_95%_CI Upper_95%_CI Number_of_Respondents
#> 1 West Virginia 54000 22.5 21.0 24.0 1061
#> 2 Mississippi 28000 24.8 23.0 26.6 906
#> 3 Oklahoma 40000 25.1 23.6 26.5 1304
diet_exercise2 <- risk_diet_exercise(
diet_exercise = "bmi is obese, high school survey",
race = "all races (includes hispanic)",
sex = "males"
)
head(diet_exercise2, n = 3)
#> State FIPS Percent Lower_95%_CI Upper_95%_CI
#> 1 West Virginia 54000 29.5 20.6 40.2
#> 2 Mississippi 28000 28.0 25.2 30.9
#> 3 Texas 48000 25.7 22.4 29.3Risk Smoking
Risk Smoking has arguments 5: smoking,
race, sex, datatype,
area.
For the following smoking arguments:
"smoking laws (any)""smoking laws (bars)""smoking laws (restaurants)""smoking laws (workplace)""smoking laws (workplace; restaurant; & bar)"
Only include the smoking argument.
race, sex, datatype,
area will be defaulted to "all races",
"both sexes", "direct estimates",
"US by State"
For the following smoking arguments:
- “smokers (stopped for 1 day or longer)”,
- “smoking not allowed at work (all people)”,
- “smoking not allowed in home (all people)”
Select a sex argument.
If "both sexes" is selected for sex, then
select a datatype argument.
If "county level modeled estimates" is selected for
datatype, then select an area argument.
race, will always be defaulted to
"all races".
datatype and area will always be defaulted
to "direct estimates", and "US by State" if
sex is “male” or “female”.
For the following smoking arguments:
"smoking not allowed at work (current smokers)""smoking not allowed at work (former/never smokers)""smoking not allowed in home (current smokers)""smoking not allowed in home (former/never smokers)"
Select a sex argument.
race, datatype, area will be
defaulted to "all races", "direct estimates",
"US by State".
For the following smoking arguments:
"former smoker; ages 18+""former smoker, quit 1 year+; ages 18+"
Select a sex and area argument.
race and datatype will be defaulted to
"all races", "direct estimates"
For the following smoking arguments:
"smokers (ever); ages 18+""e-cigarette use; ages 18+"
Select a race and sex argument.
datatype and area will be defaulted to
"direct estimates" and "US by State".
For “smokers (current); ages 18+”
Select a race and sex argument.
If "all races (includes hispanic)" is selected for
race, select a datatype argument.
If "county level modeled estimates" is selected for
datatype, then select an area argument.
datatype and area will always be defaulted
to "direct estimates", and "US by State" if
race is NOT "all races (includes hispanic)".
smoking1 <- risk_smoking(
smoking = "smokers (stopped for 1 day or longer)",
sex = "both sexes",
datatype = "county level modeled estimates",
area = "wa"
)
head(smoking1, n = 3)
#> County FIPS Percent Lower_95%_CI Upper_95%_CI
#> 1 Grant County 53025 40.8 28.2 53.8
#> 2 Kittitas County 53037 41.4 29.0 54.3
#> 3 Thurston County 53067 41.7 29.2 54.3
smoking2 <- risk_smoking(
smoking = "smoking not allowed at work (current smokers)",
sex = "both sexes",
datatype = "direct estimates"
)
head(smoking2, n = 3)
#> State FIPS Percent Lower_95%_CI Upper_95%_CI Number_of_Respondents
#> 1 Nevada 32000 55.2 43.9 65.9 55
#> 2 Wyoming 56000 57.9 47.1 68.0 69
#> 3 Utah 49000 61.2 47.5 73.3 39
smoking3 <- risk_smoking(
smoking = "smokers (current); ages 18+",
race = "all races (includes hispanic)",
sex = "both sexes",
datatype = "county level modeled estimates",
area = "wa"
)
head(smoking3, n = 3)
#> County FIPS Percent Lower_95%_CI Upper_95%_CI
#> 1 Mason County 53045 17.9 13.6 22.8
#> 2 Cowlitz County 53015 17.8 13.9 22.2
#> 3 Stevens County 53065 17.1 12.9 21.8Risk Vaccines
Risk Vaccines requires 2 arguments: vaccines and
sex
vaccines1 <- risk_vaccines(
vaccine = "percent with up to date hpv vaccination coverage, ages 13-17",
sex = "females"
)
head(vaccines1, n = 3)
#> State FIPS Percent Lower_95%_CI Upper_95%_CI Number_of_Respondents
#> 1 Mississippi 28000 32.6 23.9 42.6 48
#> 2 Wyoming 56000 48.7 38.2 59.3 70
#> 3 Kentucky 21000 48.9 37.2 60.7 59
vaccines2 <- risk_vaccines(
vaccine = "percent with up to date hpv vaccination coverage, ages 13-15",
sex = "both sexes"
)
head(vaccines2, n = 3)
#> State FIPS Percent Lower_95%_CI Upper_95%_CI Number_of_Respondents
#> 1 Mississippi 28000 35.9 27.7 45.0 59
#> 2 Wyoming 56000 44.0 34.9 53.5 79
#> 3 Texas 48000 46.4 39.6 53.3 318Risk Women’s Health
Risk Women’s Health has 4 arguments: women_health,
race, datatype, area
If "all races (includes hispanic)" is selected for
race, select a datatype argument. If any other
race is selected, then datatype and
area will be defaulted to "direct estimates"
and "US by State".
vaccines1 <- risk_vaccines(
vaccine = "percent with up to date hpv vaccination coverage, ages 13-17",
sex = "females"
)
head(vaccines1, n = 3)
#> State FIPS Percent Lower_95%_CI Upper_95%_CI Number_of_Respondents
#> 1 Mississippi 28000 32.6 23.9 42.6 48
#> 2 Wyoming 56000 48.7 38.2 59.3 70
#> 3 Kentucky 21000 48.9 37.2 60.7 59
vaccines2 <- risk_vaccines(
vaccine = "percent with up to date hpv vaccination coverage, ages 13-15",
sex = "both sexes"
)
head(vaccines2, n = 3)
#> State FIPS Percent Lower_95%_CI Upper_95%_CI Number_of_Respondents
#> 1 Mississippi 28000 35.9 27.7 45.0 59
#> 2 Wyoming 56000 44.0 34.9 53.5 79
#> 3 Texas 48000 46.4 39.6 53.3 318